Title: | Transformation Boosting Machines |
---|---|
Description: | Boosting the likelihood of conditional and shift transformation models as introduced in \doi{10.1007/s11222-019-09870-4}. |
Authors: | Torsten Hothorn [aut, cre] |
Maintainer: | Torsten Hothorn <[email protected]> |
License: | GPL-2 |
Version: | 0.3-6 |
Built: | 2024-11-19 19:22:27 UTC |
Source: | https://github.com/r-forge/ctm |
Employs maximisation of the likelihood for estimation of conditional transformation models
ctmboost(model, formula, data = list(), weights = NULL, method = quote(mboost::mboost), ...)
ctmboost(model, formula, data = list(), weights = NULL, method = quote(mboost::mboost), ...)
model |
an object of class |
formula |
a model formula describing how the parameters of
|
data |
an optional data frame of observations. |
weights |
an optional vector of weights. |
method |
a call to |
... |
additional arguments to |
The parameters of model
depend on explanatory variables in a
possibly structured additive way (see Hothorn, 2020). The number of boosting
iterations is a hyperparameter which needs careful tuning.
An object of class ctmboost
with predict
and
logLik
methods.
Torsten Hothorn (2020). Transformation Boosting Machines. Statistics and Computing, 30, 141–152.
if (require("TH.data") && require("tram")) { data("bodyfat", package = "TH.data") ### estimate unconditional model m_mlt <- BoxCox(DEXfat ~ 1, data = bodyfat, prob = c(.1, .99)) ### get corresponding in-sample log-likelihood logLik(m_mlt) ### estimate conditional transformation model bm <- ctmboost(m_mlt, formula = DEXfat ~ ., data = bodyfat, method = quote(mboost::mboost)) ### in-sample log-likelihood (NEEDS TUNING OF mstop!) logLik(bm) ### evaluate conditional densities for two observations predict(bm, newdata = bodyfat[1:2,], type = "density") }
if (require("TH.data") && require("tram")) { data("bodyfat", package = "TH.data") ### estimate unconditional model m_mlt <- BoxCox(DEXfat ~ 1, data = bodyfat, prob = c(.1, .99)) ### get corresponding in-sample log-likelihood logLik(m_mlt) ### estimate conditional transformation model bm <- ctmboost(m_mlt, formula = DEXfat ~ ., data = bodyfat, method = quote(mboost::mboost)) ### in-sample log-likelihood (NEEDS TUNING OF mstop!) logLik(bm) ### evaluate conditional densities for two observations predict(bm, newdata = bodyfat[1:2,], type = "density") }
Employs maximisation of the likelihood for estimation of shift transformation models
stmboost(model, formula, data = list(), weights = NULL, method = quote(mboost::mboost), mltargs = list(), ...)
stmboost(model, formula, data = list(), weights = NULL, method = quote(mboost::mboost), mltargs = list(), ...)
model |
an object of class |
formula |
a model formula describing how the parameters of
|
data |
an optional data frame of observations. |
weights |
an optional vector of weights. |
method |
a call to |
mltargs |
a list with arguments to be passed to
|
... |
additional arguments to |
The parameters of model
depend on explanatory variables in a
possibly structured additive way (see Hothorn, 2020). The number of boosting
iterations is a hyperparameter which needs careful tuning.
An object of class stmboost
with predict
and
logLik
methods.
Torsten Hothorn (2020). Transformation Boosting Machines. Statistics and Computing, 30, 141–152.
if (require("TH.data") && require("tram")) { data("bodyfat", package = "TH.data") ### estimate unconditional model m_mlt <- BoxCox(DEXfat ~ 1, data = bodyfat, prob = c(.1, .99)) ### get corresponding in-sample log-likelihood logLik(m_mlt) ### estimate conditional transformation model bm <- stmboost(m_mlt, formula = DEXfat ~ ., data = bodyfat, method = quote(mboost::mboost)) ### in-sample log-likelihood (NEEDS TUNING OF mstop!) logLik(bm) ### evaluate conditional densities for two observations predict(bm, newdata = bodyfat[1:2,], type = "density") }
if (require("TH.data") && require("tram")) { data("bodyfat", package = "TH.data") ### estimate unconditional model m_mlt <- BoxCox(DEXfat ~ 1, data = bodyfat, prob = c(.1, .99)) ### get corresponding in-sample log-likelihood logLik(m_mlt) ### estimate conditional transformation model bm <- stmboost(m_mlt, formula = DEXfat ~ ., data = bodyfat, method = quote(mboost::mboost)) ### in-sample log-likelihood (NEEDS TUNING OF mstop!) logLik(bm) ### evaluate conditional densities for two observations predict(bm, newdata = bodyfat[1:2,], type = "density") }